{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d3612ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import integrate\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28b87552",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56fc6e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5351b6d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024834905660377354"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T=100\n",
    "L=3\n",
    "mu=0\n",
    "d = 2.34  \n",
    "l = 2\n",
    "s = 8.39e-2\n",
    "d * (L * (1 - mu) / T) ** l / (s + (L * (1 - mu) / T) ** l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "afb35607",
   "metadata": {},
   "outputs": [],
   "source": [
    "class environment():\n",
    "    def __init__(self,a,ar_ratio):\n",
    "        self.a=a\n",
    "        self.ar=self.a*ar_ratio\n",
    "        self.b = 3.846e-9\n",
    "        \n",
    "        self.alpha_sr=0.7\n",
    "        self.alpha_rs=0.9\n",
    "        \n",
    "        \n",
    "    def state_eq(self, t, x):\n",
    "        Ts = x[0]\n",
    "        Tr = x[1]\n",
    "\n",
    "        T = Ts + Tr\n",
    "        \n",
    "\n",
    "        dts = max(self.a * Ts * (1 - self.b * (Ts + self.alpha_sr * Tr)) - 1, 0) \n",
    "\n",
    "        dtr = max(self.ar * Tr * (1 - self.b * (self.alpha_rs * Ts + Tr)) - 1, 0)\n",
    "        \n",
    "        return np.array([dts, dtr])\n",
    "    \n",
    "    def gen_state(self,t1):\n",
    "\n",
    "        t0=0    # start and end\n",
    "        interval=t1*24+1\n",
    "        t = np.linspace(t0, t1,interval)  # the points of evaluation of solution\n",
    "        x0 = [1e4,4e1]    # initial value\n",
    "        x = np.zeros((len(t), len(x0)))   # array for solution\n",
    "        x[0, :] = x0\n",
    "        r = integrate.ode(self.state_eq).set_integrator(\"dopri5\")\n",
    "        r.set_initial_value(x0, t0)\n",
    "        for i in range(1, t.size):\n",
    "            x[i, :] = r.integrate(t[i])\n",
    "        return t,x\n",
    "    \n",
    "def cal_time(x_,tumor):\n",
    "    for i in range(len(x_.T[0])):\n",
    "        if x_.T[0][i]+x_.T[1][i]>tumor:\n",
    "            break\n",
    "    return i\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "288d64f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "tcga_all=pd.read_csv('tcga_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec004135",
   "metadata": {},
   "outputs": [],
   "source": [
    "ar_ratio_l=[(1.3,1.4),(1.4,1.5),(1.5,1.6),(1.6,1.7),(1.7,1.8),(1.8,1.9),(1.9,2.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a028de6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.3, 1.4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:41<00:00,  2.56it/s]\n",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.4, 1.5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:41<00:00,  2.56it/s]\n",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.5, 1.6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:40<00:00,  2.58it/s]\n",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.6, 1.7)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:40<00:00,  2.57it/s]\n",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.7, 1.8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:43<00:00,  2.53it/s]\n",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.8, 1.9)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:40<00:00,  2.57it/s]\n",
      "  0%|                                                   | 0/414 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.9, 2.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [05:13<00:00,  1.32it/s]\n"
     ]
    }
   ],
   "source": [
    "res_prop_all=[]\n",
    "\n",
    "for (l_,u_) in ar_ratio_l:\n",
    "    print((l_,u_))\n",
    "    res_prop=[]\n",
    "    for i in trange(len(tcga_all)):\n",
    "        tumor=tcga_all.loc[i,'tumor_size']\n",
    "        a=tcga_all.loc[i,'a']\n",
    "        ar_ratio=np.random.uniform(l_,u_)\n",
    "        env=environment(a,ar_ratio)\n",
    "        t1=600\n",
    "        t,x=env.gen_state(t1)\n",
    "        j=cal_time(x,tumor)\n",
    "        res_prop.append(x[j][1]/tumor)\n",
    "    res_prop_all.append(res_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf591581",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 414/414 [02:43<00:00,  2.54it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "res_prop=[]\n",
    "for i in trange(len(tcga_all)):\n",
    "    tumor=tcga_all.loc[i,'tumor_size']\n",
    "    a=tcga_all.loc[i,'a']\n",
    "    ar_ratio=np.random.uniform(1.3,1.8)\n",
    "    env=environment(a,ar_ratio)\n",
    "    t1=600\n",
    "    t,x=env.gen_state(t1)\n",
    "    j=cal_time(x,tumor)\n",
    "    res_prop.append(x[j][1]/tumor)\n",
    "res_prop_all.append(res_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f769f59e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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VrD9Kls/nS2o/1WFzw4YNMUETAPr06YMVK1ZgypQpag8Xt56PEKLTNX5GjhyJkSNHRn8+7bTTsG/fPjzwwAOdhs1FixahsrIy+rPX60VZWRkqKipixn0SERER5br/7KlHa4Kxlw5JBj7cBgAoGDoBHmfihNO7yI3xx/bUs4iW9OnBJhzxBrvfMQnJ1kUqJpT3REm+W7PjUfaK9Bbtjuqw6fF40NzcHLe9paUFbnfyb86+ffvC4XDEtWLW1NTEtXZ25dRTT8UzzzzTZXk9nvjuHC6XCy6XK+nnISIiIspmsiLglwCbI/7y0KYcbQiw2Z2wdTJPR2sYvL5KwOVyweaQu98xCcnWRSrcbjfrj5KS7PtE9ZxT55xzDq6//nr8+9//hhACQgh88MEHmDt3Ls4999ykj+N2uzFx4kSsX78+Zvv69esxefLkpI+zZcsWDBgwIOn9iYiIiCheOpMDRUQmCaJYTotM8+rkBEGkMdUtm4888giuvPJKnHbaadFEK0kSzj33XPzqV79SdazKykpcfvnlmDRpEk477TQ89thj2Lt3L+bOnQugrQvsgQMHsGbNGgBts9UOGTIEY8eORSgUwjPPPIM//elP+NOf/qT2zyAiIiKidrwarZPpDYQ5I20HVpnl1SrlJOtQHTZ79uyJV155BV988QU+++wzCCEwZswYDB8+XPWTz549G3V1dVi2bBmqq6sxbtw4rF27FuXl5QCA6upq7N27N7p/KBTC7bffjgMHDiA/Px9jx47Fq6++irPOOkv1cxMRERHRUVqtk9kckNC/hyaHyhouhzVCnMsiLbBkHSmtswkAI0aMwIgRI9IuwLx58zBv3ryEv3vqqadifl6wYAEWLFiQ9nMSERERUSytWja1Cq3ZxAothnY7YLdAOclaVIdNIQReeukl/POf/0RNTQ0URYn5/csvv6xZ4YiIiIhIf4oi0BrUqmWT6zR25HKYv8XQKuNKyVpUh81bb70Vjz32GKZNm4bS0tJOlykhIiIiImtoCaU/OVBEMKwgKMmaLslhdVaYeMdpka6+ZC2qw+YzzzyDl19+meMkiYiIiLKE1l1fWwISPEUMmxFOC7RsWqH1laxH9buqpKQExx13nB5lISIiIiIDaN311ctxmzGsMEGQFVpfyXpUh80lS5Zg6dKl8Pv9epSHiIiIiDKsRYeWTTrKCq2GVigjWY/qbrQXXXQRnnvuOfTv3x9DhgyJrrUZsXnzZs0KR0RERET6EkJo3o22OchJgtpzOeyw2aDZuFg9uJ0Mm6Q91WHzqquuwqZNm3DZZZdxgiAiIiIii/OHZciKtinIF2w7phWW/MgUl8OOkKR0v6NB2LJJelAdNl999VWsW7cO3/rWt/QoDxERERFlkF5dXluCEkryXd3vmCPMHzZ5Y4C0p/oWRllZGYqLi/UoCxERERFlWLNG62t21KLTca3K7TR3mHOzZZN0oPpd9eCDD2LBggXYs2ePDsUhIiIiokzSrWWTkwTFMHs3VY7ZJD2o7kZ72WWXwefzYdiwYSgoKIibIKi+vl6zwhERERGRvlp1a9nkJEHtmT3MmT0MkzWpDptVVVU6FIOIiIiIMk1WBHwhWZdjtwT1Oa5Vmb2bqtnDMFmT6rB55ZVX6lEOIiIiIsqw1pB+XV3DkoKgJMPjdOj2HFZi5pZDu93c5SPrUh029+7d2+XvBw8enHJhiIiIiChz9OpCe/T4DJsRHhO3HDJokl5Uh80hQ4Z0ubamLLPLBBEREZEV6B82JfQudOv6HFZh5m6qZu/iS9alOmxu2bIl5udwOIwtW7bgoYcewi9/+UvNCkZERERE+mrVeVylnt10rcbMLbwel3nLRtamOmyeeOKJcdsmTZqEgQMH4v7778f3v/99TQpGRERERPrKRDdaasOWTcpFmr2zjj/+ePznP//R6nBEREREpCNFEfCH9Q2DPrZsRjnsNjgcnQ9FM5KZgzBZm+qWTa/XG/OzEALV1dVYsmQJRowYoVnBiIiIiEg/AUmGEPo+RzCsQFYEHHZzhqxM8zjt8JlwfhMzT15E1qY6bPbs2TNugiAhBMrKyvDHP/5Rs4IRERERkX70Wl8z/nkk9MhzZeS5zM7jtMNnwq7FHhfDJulDddj85z//GfOz3W5Hv379MHz4cDidqg9HRERERAbIVOjxh2SGzf9pmyQobHQx4ngcnCCI9KE6HdpsNkyePDkuWEqShHfeeQff/va3NSscEREREelD7/GaEZlqQbUCs3ZXZcsm6UX1O2vatGmor6+P297U1IRp06ZpUigiIiIi0lemwmamnscKzLr8CWejJb2ofmcJIeLGbAJAXV0dCgsLNSkUEREREekrUzPFMmweZcYWRLfTDjsncCKdJN2NNrJ+ps1mw1VXXQWPxxP9nSzL+OSTTzB58mTtS0hEREREmguGlYw8T4DdaKPM2I3WjGWi7JF02CwpKQHQ1rLZo0cP5OfnR3/ndrtx6qmn4rrrrtO+hERERESkqZDUtiRJJgQkhs0IM3ajzXOZr0yUPZIOm7///e8BAEOGDMHtt9/OLrNEREREFpXJAKgoQFCSTRm0Ms2MrYhm7NpL2UP1bLSLFy/WoxxERERElCGBDI+jDIQVhk0AdrsNbqcdISkzXZiTwXohPfFWBhEREVGOydR4zejzsSttlNlaN81WHsoufHcRERER5ZhMh79Mh1szM9sYSbOVh7ILwyYRERFRjglkvGWTYTPCbGMk80xWHsouqt9da9asQTAYjNseCoWwZs0aTQpFRERERPoJyZkNf2Yao2i0PJONkeSYTdKT6rB59dVXo6mpKW57c3Mzrr76ak0KRURERET6yXT445jNo8zUbdXpsMFhtxldDMpiqsOmEAI2W/ybcv/+/dG1OImIiIjIvDIdNsNyZtb0tAIzTchjpuBL2SnppU8mTJgAm80Gm82G6dOnw+k8+lBZlrF7925897vf1aWQRERERKSdMLvRGsZMAc9MZaHslHTYPP/88wEAW7duxcyZM1FUVBT9ndvtxpAhQ3DhhRdqXkAiIiIi0k5YViAy3NCY6XBrZuZq2TRPWSg7JR02Fy9eDAAYMmQIZs+ejby8PN0KRURERET6MCL4yYqAogjYOT4QdrsNHpfdFMvBmG2yIso+SYfNiCuvvBJA2+yzNTU1UJTYD8rgwYO1KRkRERERac6o8ZMhWUGeneEGaOu+aoqwyW60pDPVYfOLL77ANddcg/fffz9me2TiIFnmbGNEREREZmVUl1ZJ4SRBEXlOB5oQNroY7EZLulMdNq+66io4nU78/e9/x4ABAxLOTEtERERE5iQbFPokjtuMMkvIY8sm6U112Ny6dSs2bdqEUaNG6VEeIiIiItIRWzaNZ4aQZ7OZa7Iiyk6q32FjxoxBbW2tHmUhIiIiIp0Z17LJsBnhMUHLpsfpYA9F0p3qd/q9996LBQsW4O2330ZdXR28Xm/MPyIiIiIyL6NaGOVMr7diYvkmaNk0S1deym6q32UzZszABx98gOnTp6N///7o1asXevXqhZ49e6JXr16qC7Bq1SoMHToUeXl5mDhxIjZu3JjU49577z04nU6cdNJJqp+TiIiIKFcZ1bIps2UzygzdaM1QBsp+qsds/vOf/9TsyZ9//nncdtttWLVqFaZMmYJHH30Us2bNwvbt27tcQqWpqQlXXHEFpk+fjsOHD2tWHiIiIqJsZ1jYZMtmlMthh8NhMzSAM2xSJqgOm1OnTtXsyR966CFce+21mDNnDgCgqqoK69atw+rVq7F8+fJOH3fDDTfgkksugcPhwF/+8pcunyMYDCIYDEZ/jnT1DYfDCIeNn3KaiIiIKJPC4TCELKl6jFDkdv+XIFIISaEQr73ac9sEfCrrAdCmLgDAZVNYH5SyZN87qsNmhM/nw969exEKhWK2jx8/PqnHh0IhbNq0CQsXLozZXlFREbeGZ3u///3v8dVXX+GZZ57BPffc0+3zLF++HEuXLo3b/sYbb6CgoCCpshIRERHlsqAMRC4bfbu3QE6hUeyzXcBnmpYqN2lRFwCwdRewVatCUc7x+XxJ7ac6bB45cgRXX301XnvttYS/l2U54faOamtrIcsySktLY7aXlpbi0KFDCR/zxRdfYOHChdi4cSOczuSKvmjRIlRWVkZ/9nq9KCsrQ0VFBYqLi5M6BhEREVG22HagCXXNwe53bMchycCH2wAABUMnwONUn3AG9MrHyNIeqh+XrXYebsbBBr/qx2lRFwBw8nG9UeBOud2JclyyE8OqfofddtttaGhowAcffIBp06bhz3/+Mw4fPox77rkHDz74oOqCdpxyWQiRcBpmWZZxySWXYOnSpTj++OOTPr7H44HH44nb7nK54HK5VJeXiIiIyNLsDtgc6i4BbcrRazOb3QmbQ33AsdkdvPZqpyjfA5tXfTdWLeqi7fnz4LBz6RNKTbKfZdVh8x//+AdeeeUVfPOb34Tdbkd5eTnOPPNMFBcXY/ny5Tj77LOTOk7fvn3hcDjiWjFramriWjsBoLm5GR999BG2bNmCm266CQCgKAqEEHA6nXjjjTdwxhlnqP1ziIiIiHKKYtAEQZwfKJaRE/S4nXYGTcoI1UuftLa2on///gCA3r1748iRIwCAE044AZs3b076OG63GxMnTsT69etjtq9fvx6TJ0+O27+4uBjbtm3D1q1bo//mzp2LkSNHYuvWrTjllFPU/ilEREREOceozKcwbcYwMmzmuzkTLWWG6pbNkSNH4vPPP8eQIUNw0kkn4dFHH8WQIUPw29/+FgMGDFB1rMrKSlx++eWYNGkSTjvtNDz22GPYu3cv5s6dC6BtvOWBAwewZs0a2O12jBs3Lubx/fv3R15eXtx2IiIiMi9fSP0MnGpxLFrn2LJpDvlGhk0ue0IZktKYzerqagDA4sWLMXPmTPzhD3+A2+3GU089pepYs2fPRl1dHZYtW4bq6mqMGzcOa9euRXl5OQCguroae/fuVVtEIiIiMrExd6/T/Tn2rEhuWE8uMirzMWvGinRlNWLd0zyX6s6NRClRHTYvvfTS6P8nTJiAPXv24LPPPsPgwYPRt29f1QWYN28e5s2bl/B33YXXJUuWYMmSJaqfk4iIiChXGdXCyG608fJcDrQG9W/pT/S8RJmgOmwuW7YMt99+e3SNyoKCAnzjG9+A3+/HsmXLcPfdd2teSCIiIsoe25fNTGq/+pYQPtxdh8oXPwEAvHzjZIwawKUz0iXYxmga+W5jwia70VKmqG5DX7p0KVpaWuK2+3w+LF26VJNCERERUfYqcDuT+mez2+LWEEz2sdQFg7ImGzbjGRX6OEEQZYrqsNnZOpgff/wxevfurUmhiIiIiPwhOebngCR3sieRNRkVNvOcDJuUGUnf+uvVqxdsNhtsNhuOP/74mMApyzJaWlqis8gSERERpSsQ7hA2QwyblF3y3JmfqCfP5YCda2xShiQdNquqqiCEwDXXXIOlS5eipKQk+ju3240hQ4bgtNNO06WQRERElHt8HVs2w3KnPazICtiPtiMjWjbzDQi4lLuSDptXXnklAGDo0KGYMmUKnE6OhyAiIiL9+Du0bAoBBMIKx5tZFm8SdGRE2ORMtJRJqm9t9OjRAzt27Ij+/Morr+D888/HHXfcgVAopGnhiIiIKDeFZQVhSYnb7gtlfubOrMPMZxpOhx0uZ2ZbGjkTLWWS6nf3DTfcgJ07dwIAdu3ahdmzZ6OgoAAvvvgiFixYoHkBiYiIKPd07ELb3XYyP/Z+TizT4Y89AyiTVIfNnTt34qSTTgIAvPjii5g6dSqeffZZPPXUU/jTn/6kdfmIiIgoB3WciTa6PcywmS4bmzZNJeNhky2blEEpLX2iKG3dWt58802cddZZAICysjLU1tZqWzoiIiLKSa2ddJdtDbIbbbqMamFkxE0s0y2NHLNJmaQ6bE6aNAn33HMPnn76aWzYsAFnn302AGD37t0oLS3VvIBERESUezpt2WQ32rQZFfo4i3BimQybDruNYZMySnXYrKqqwubNm3HTTTfhzjvvxPDhwwEAL730EiZPnqx5AYmIiCj3dNaC6Q/LUBQuoZEOo0Ifl3ZMLJPdWhk0KdNUr18yfvx4bNu2LW77/fffD4eDb2AiIiJKjxCi04mAhAB8YRlFHi7BliqjQh/HiiZWkMGWzUw+FxGQQtjsTF5enlaHIiIiohwWlBTIXbRe+oISw2YaDBuzyayZkMdph90OKPEr/WiOM9EmR+8llgrcuXP+Suov7d27N3bu3Im+ffuiV69eXXa/qK+v16xwRERElHu6mwSoleM202JUN1qGzcRstrZxlL6g/u9rzkSbnDF3r9P1+HtWnK3r8c0kqbD58MMPo0ePHtH/c4A3ERER6aW7tTQ5I216jLqKs/P6sVP5GQqb7EZLmZZU2Lzyyiuj/7/qqqv0KgsRERFRp8ueRHQXRqlrRoU+hs3OFbidqENI9+dhN9rkbF82M6n9fCEJk+55CwDw0V3Tc6p7bLJUvyKbN2+Gy+XCCSecAAB45ZVX8Pvf/x5jxozBkiVL4Ha7NS8kERER5Y7Wblp4uguj1DXjwqYhT2sJmWhxtNmAPCfDZjJSCY0FbifDZgKqlz654YYbsHPnTgDArl27MHv2bBQUFODFF1/EggULNC8gERER5ZbuJueQZYFAmK2bqTJugiCmzc5kYkmSPJcDdiZ+yjDVYXPnzp046aSTAAAvvvgipk6dimeffRZPPfUU/vSnP2ldPiIiIsohkqwgGO5+Wk52pU2dw6DAwZzTuUy0bLILLRlBddgUQkD539zMb775Js466ywAQFlZGWpra7UtHREREeWUZGea5SRBqTOqG61RIdcK8l0O3VucOTkQGUF12Jw0aRLuuecePP3009iwYQPOPrtt6t7du3ejtLRU8wISERFR7vAnGTbZspk6h+qrP21wgqDO2e023bvSFrg4npAyT/XppqqqCps3b8ZNN92EO++8E8OHDwcAvPTSS5g8ebLmBSQiIqLckezkP3ovup7NjBo7yfGCXdO7myu70ZIRVN/iGD9+PLZt2xa3/f7774fDwTcxERERpY4tm/pzGNWNli2bXcrXuWWTYZOMkFJHisbGRjz++ONYtGgR6uvrAQDbt29HTU2NpoUjIiKi3JJsiAyEZSiK0Lk02cmwCYIM6r5rFXqPqSzIwIy3RB2pbtn85JNPMH36dPTs2RN79uzBddddh969e+PPf/4zvv76a6xZs0aPchIREVEOSLZ7rBBAQJK5rl0KjOrOypbNrunZ8shlT8goqu8xVVZW4uqrr8YXX3yBvLy86PZZs2bhnXfe0bRwRERElDvCsgJJTr61MtkutxTLaVTYZNjpkp43TtiFloyiOmz+5z//wQ033BC3fdCgQTh06JAmhSIiIqLco3YcJsdtpoZLn5iTnmM2uewJGUV12MzLy4PX643b/vnnn6Nfv36aFIqIiIhyTyCsLjyq3Z/aGBX6GDa75rDb4HHpM7CVYZOMovodfd5552HZsmUIh8MA2qbP3rt3LxYuXIgLL7xQ8wISERFRblDbLdbPsJkShk3z0isUshstGUV12HzggQdw5MgR9O/fH36/H1OnTsXw4cPRo0cP/PKXv9SjjERERJQDApLalk1Fp5JkN8PGbHKCoG7lu/QZt6n3sipEnVH9ji4uLsa7776Lf/zjH9i8eTMURcE3vvENzJgxQ4/yERERUY5QGx7ZjTY1RrUwOh1c+6Q7erVsctZmMoqqd54kScjLy8PWrVtxxhln4IwzztCrXERERJRj1IbHkKRAUQSXdFDJiJZNp4N1lAw9uru6nXZ2YSbDqLrF5HQ6UV5eDlnmnUQiIiLSVlBS3y02lcfkOiOCB8NOcvQIm5wciIykuj/DXXfdhUWLFqG+vl6P8hAREVEOUhSBcEphkzfA1bLZbHBkuKXRaWcX2mToMbYyj+M1yUCqO3A/8sgj+PLLLzFw4ECUl5ejsLAw5vebN2/WrHBERESUG1JtoWTLZmpcdntGe6q52I02KS6HHS6nPaUbL51hyyYZSXXYPP/883UoBhEREeWyUIoX16k+Ltc5HTYgnLnnYzfa5OW7HBqHTU4ORMZR/e5bvHixHuUgIiKiHBZMsZWNLZupyXRLo4sz0SatwO2A16/dnQAue0JG4iefiIiIDBeWRUqPY8tmajI9hpJhM3laj7HMc/O1J+Pw3UdERESGSzU0hmWGzVRkeikSLn2SPC1npHXYbfA42bJJxmHYJCIiIsOlGhoZNlPjznBLo4uz0SZNy26vnImWjJbUJ9/r9epdDiIiIsphKU8QxLCZEmemw6aTLZvJynNpVzdaHosoFUm9A3v16oWamhoAwBlnnIHGxkbNCrBq1SoMHToUeXl5mDhxIjZu3Njpvu+++y6mTJmCPn36ID8/H6NGjcLDDz+sWVmIiIjIGJKS2phNKcWxnrmOEwSZV56G3V617JJLlIqkZqMtKipCXV0d+vfvj7fffhvhsDYzZD3//PO47bbbsGrVKkyZMgWPPvooZs2ahe3bt2Pw4MFx+xcWFuKmm27C+PHjUVhYiHfffRc33HADCgsLcf3112tSJiIiIso8KcUWSklhy2YqMt6NlmGzW76QFP2/gEAonPi9HZTkhP9PRCixx+UyKJRpSb3jZsyYgWnTpmH06NEAgAsuuAButzvhvv/4xz+SfvKHHnoI1157LebMmQMAqKqqwrp167B69WosX748bv8JEyZgwoQJ0Z+HDBmCl19+GRs3buw0bAaDQQSDwejPkS7B4XBYs9BMRERE6QmGwhCyFLddKHK7/0sQHVoyZRkIBENcx1ElO+SEr3dnuquHbp9PkcHLrq6NufsN1Y+pfPETVft/8YsK1c9BnQuHpXb/DyNsy52eFsnmqKTC5jPPPIP/9//+H7766its2LABY8eORUFBQVoFDIVC2LRpExYuXBizvaKiAu+//35Sx9iyZQvef/993HPPPZ3us3z5cixdujRu+xtvvJH230BERET6CspA5HLFt3sL5AS9AtftymiRclIy9dCVf7COkqB/q+PatWt1f45c0v5zsW7dG/DkUK9ln8+X1H5Jvavz8/Mxd+5cAMBHH32Ee++9Fz179ky5cABQW1sLWZZRWloas720tBSHDh3q8rHHHnssjhw5AkmSsGTJkmjLaCKLFi1CZWVl9Gev14uysjJUVFSguLg4rb+BiIiItLHxiyOQE7SWOSQZ+HAbAKBg6ISEyzicfFxvdg9UKSwreO+L2qT3T6YeOn2s3YbTj++nuoy55jszjraS7T7Siv0NiS/mg5KCn/zpUwDAgxeOhcfZeRflk4f2gafdJEH8nGjLF5Kw4MO2Xp0zZ1bk1Oub7ASyql+Rf/7zn9H/C9H2pWCzpd51peNjhRDdHm/jxo1oaWnBBx98gIULF2L48OH40Y9+lHBfj8cDj8cTt93lcsHlcqVcbiIiItKOAgdsCfKLTTl6TWCzO2FzJNjJ7uR3ukouF2B3OiGS7PWXVD10wuN2sH6SUNLuNeoTFKj1JR6PabMf3Z7ndsPTyfImNhvQr6Qgret06ppLHH1t27JF7oTNZD/TKY3WXrNmDU444QTk5+cjPz8f48ePx9NPP63qGH379oXD4YhrxaypqYlr7exo6NChOOGEE3Dddddh/vz5WLJkido/gYiIiExCTnEmWq0en6vcXbSIWfF5solHgyVL3E47gyYZTvU7+aGHHsKNN96Is846Cy+88AKef/55fPe738XcuXNVLUPidrsxceJErF+/Pmb7+vXrMXny5KSPI4SImQCIiIiIrCXdGWUZNlOjpitsOjI982028KhoOe70GBmqX6KuqG7r/fWvf43Vq1fjiiuuiG4777zzMHbsWCxZsgTz589P+liVlZW4/PLLMWnSJJx22ml47LHHsHfv3uj40EWLFuHAgQNYs2YNAGDlypUYPHgwRo0aBaBt3c0HHngAN998s9o/g4iIiEwi3dVLGDZTw5ZN89KiZbOrsZxEmaI6bFZXVydseZw8eTKqq6tVHWv27Nmoq6vDsmXLUF1djXHjxmHt2rUoLy+PPtfevXuj+yuKgkWLFmH37t1wOp0YNmwYVqxYgRtuuEHtn0FEREQmISc7cLATSpqPz1WZCiMMPepp0RqsRWAlSpfqsDl8+HC88MILuOOOO2K2P//88xgxYoTqAsybNw/z5s1L+Lunnnoq5uebb76ZrZhERERZhmM2jZGxsNnJBDbUObvdBpfTjrCUerM/uy+TGagOm0uXLsXs2bPxzjvvYMqUKbDZbHj33Xfx1ltv4YUXXtCjjERERJTFlDTDIls2U5OpEMiWzdS4HWmGTb7uZAKq34UXXngh/v3vf6Nv3774y1/+gpdffhl9+/bFhx9+iAsuuECPMhIREVEWS7cbLVs2U5OpEJjHls2UpBsWGTbJDFJaDGbixIl45plntC4LERER5aB0WyaZNVPDMZvmlm43WHajJTPgu5CIiIgMle5stOxGm5pMtDg67Da4GHpS4nKmt0YmX/fMSnc4QLbiu5CIiIgMlX7LJi/yUuFy2OGwpxdousMZUVPntKf32jFsZla6wwGyFd+FREREZKh0wyLHbKZO7zDI8ZqpS7cbrMuh740EisXzUGIMm0RERGSodLvRskEhdXqHwTwnw2aqHGmERYfdBpuNYTOTeB5KjGGTiIiIDMVutMbROwzmsRttylxpdHF2slUz49iNNjHVs9G2trZixYoVeOutt1BTUwOlw+3IXbt2aVY4IiIiyn6cjdY4+W59w6bex89m6Yyn1XssLsXjTa/EVIfNOXPmYMOGDbj88ssxYMAANtETERFRWtINi7zIS10+u9GaVjoTBKU7uRCpJ9IcDpCtVIfN1157Da+++iqmTJmiR3mIiIgox4g0w2K6j89lendzZctm6tLJi5yINjPan3s4QVBiqt+KvXr1Qu/evfUoCxEREeWg9Fs2tSlHLtJzgiCbDfA4mXpSlU5XWPY8zIz2AZPnocRUnwF+8Ytf4O6774bP59OjPERERJRj0h6zyau8lHmc9rRa0LqS53Iw9KTBnsZr5+DrnhHtTz3sYZGY6m60Dz74IL766iuUlpZiyJAhcLlcMb/fvHmzZoUjIiKi7McJgoxjs9mQ53TAF5I1PzbX2ExPOnkxnaBKyWt/7uJstImpDpvnn3++DsUgIiKiXJXuNRonCEpPnluvsMkutOmwIZ1utBoWhDrV/tTDm16JqQ6bixcv1qMcRERElKPSnViDYTM9es1Iq/dMt9mOgdH82p972J0/MdVhM2LTpk3YsWMHbDYbxowZgwkTJmhZLiIiIsoR6YZFzgKZHt3CJmeipSzX/tzFe16JqQ6bNTU1uPjii/H222+jZ8+eEEKgqakJ06ZNwx//+Ef069dPj3ISERFRlko3bPIiLz16hUK2bFK2i5kgCDwRJaK6M/3NN98Mr9eLTz/9FPX19WhoaMB///tfeL1e3HLLLXqUkYiIiLJY+kuf8CIvHXpN5MMJgijrccxmt1S3bL7++ut48803MXr06Oi2MWPGYOXKlaioqNC0cERERJT90u0GK0TbeCl7GusS5jI9WiDtdq6xmS7eQzG/mAmC2LKZkOqzgKIoccudAIDL5YKiKJoUioiIiHKHFhNrcNmB1Lmddjg0DupcYzN96bTYs7U/M9q/ynzJE1MdNs844wzceuutOHjwYHTbgQMHMH/+fEyfPl3TwhEREVH20yIo8uI6PVp3eWUX2vSl857mpFmZIWLqiK95IqrD5m9+8xs0NzdjyJAhGDZsGIYPH46hQ4eiubkZv/71r/UoIxEREWUxLS6MeXGdHq0nCeLkQOlL5y3Nj0NmsGWze6rHbJaVlWHz5s1Yv349PvvsMwghMGbMGMyYMUOP8hEREVGW06JVkmEzPXkubcdXMmymL533NFv6M6P9y8xXPLGU19k888wzceaZZ2pZFiIiIt34QpLuz1HgTvlrNWcJIaDFlA+cNiI9WodDdqNNXzphU5IZfTKBy510L6lvxUceeQTXX3898vLy8Mgjj3S5L5c/ISIiMxpz9zrdn2PPirN1f45so1WLJCcISo/W4ZAtm+mT0riDks5jibSUVNh8+OGHcemllyIvLw8PP/xwp/vZbDaGTSIiIkqapFHY5MV1evKc2oZDj8bdcnNROq2TWn2uSAW+5AklFTZ3796d8P9ERERWsX3ZzKT39YUkTLrnLQDAR3dNZ/dYHWk1toxZMz1ahkObjWtsaiEsp/6mlmXBtWfJFFSfCZYtWwafzxe33e/3Y9myZZoUioiISGsFbmfS/9p3AVTzOFKPLZvm4HHaYdcoH3qcXGNTC+mETQAIpfl4Ii2oPq0sXboULS0tcdt9Ph+WLl2qSaGIiIiMxJlNM0fR6LVm1kyPzWaDR6OutFrPbJurQlJ6n410wyqRFlSfDYQQCe9Wffzxx+jdu7cmhSIiIjISJ5vJHLZsmodWXV+1Cq25Lt2WyZDEzwQZL+k+P7169YLNZoPNZsPxxx8fEzhlWUZLSwvmzp2rSyGJiIgyibklczSbjZat0WlrC4nh9I/Dlk1NBMNyeo9n2Mws9hxPKOmwWVVVBSEErrnmGixduhQlJSXR37ndbgwZMgSnnXaaLoUkIiLKJLZsZo52LZuss3RpFRI5OZA20g2LDJtkBkmHzSuvvBKS1LYg9owZM3DsscfqVigiIiIjsZUsc7Qas8k6S5/bwW60ZqEoIu1usEEpvZZRIi2oOqs4nU7MmzcPssw3LxERZS92o80crVokGTbT59aoRVKr4+SygAZB0R/i9brebOw72y3VZ4NTTjkFW7Zs0aMsREREpsDJZjJHszGb7PqcNoZN8wiE0z8HaXEMSh5jZ2KqFwWbN28efvKTn2D//v2YOHEiCgsLY34/fvx4zQpHRERkBGbNzOEEQeahVUh0OXjZnS5/mpMDAUBAg2NQ17icbPdUh83Zs2cDAG655ZboNpvNFl0ShV1siYjI6tiymTkMm+ah1ZhNrY6Ty/whKe1jyIpAICwjz8UxtHppnzUZPBNTHTZ3796tRzmIiIhMQ5KPBhfB7pm6Ytg0D5cGIdHpsCVcj53U8Wk03tIfYtjMHL7vE1EdNsvLy/UoBxERkWlI4mjLJpfU0JdWYy0ZNlPj69CCFpblhN3I289s2tUspzabI+6YBW7Vl5s5rzWoTdhsDUnoVejW5FgUjzdWupfSp/+rr75CVVUVduzYAZvNhtGjR+PWW2/FsGHDtC4fERFRxrVv2Wz/f9IeJwgy1pi716l+TOWLn6jaf8+Ks1U/Ry4TQsAfTr8bLaBdCyl1j7kzMdX9JdatW4cxY8bgww8/xPjx4zFu3Dj8+9//xtixY7F+/Xo9ykhERJRRYflo006Y4zd1pVU3ZXZ3pmzhDyduXU5Fa1Cb0EqJ2Tr5Px2lumVz4cKFmD9/PlasWBG3/Wc/+xnOPPNMVcdbtWoV7r//flRXV2Ps2LGoqqrC6aefnnDfl19+GatXr8bWrVsRDAYxduxYLFmyBDNnzlT7ZxAREXUqJmxKDDF60qplU1EQnayQkrd9Wew11H9216MlEB9QgpIcbdF86KLx8DgTjwPs28ODE44t0b6gOaRFw4Co5bEoHk833VPdsrljxw5ce+21cduvueYabN++XdWxnn/+edx222248847sWXLFpx++umYNWsW9u7dm3D/d955B2eeeSbWrl2LTZs2Ydq0afje977HdT+JiEhTQald2JTZsqknLYdactimegVuZ8y/ojwnPC5H/L924dLjTPD7//0r9DjjjknqJAr7qQqGFZ7DMsTGts2EVIfNfv36YevWrXHbt27div79+6s61kMPPYRrr70Wc+bMwejRo1FVVYWysjKsXr064f5VVVVYsGABvvnNb2LEiBH4v//7P4wYMQJ/+9vf1P4ZREREnQq3C5shiRdqetKy+6vCrrRpS7dl2M6mnrRp3RqpZXglUkv17abrrrsO119/PXbt2oXJkyfDZrPh3Xffxb333ouf/OQnSR8nFAph06ZNWLhwYcz2iooKvP/++0kdQ1EUNDc3o3fv3p3uEwwGEQwGoz97vV4AQDgcRjgcTrq8RESUGxRFxHw/BIIhfl/oSJIkiC7W6BaK3O7/EkQXEzaFQiGgk+6dlBybIkPI8eEk6XpQZH5e0uRtDWj2mQCAptYAity8CaAHud1nRZGlnHrvJ/u3qg6bP//5z9GjRw88+OCDWLRoEQBg4MCBWLJkCW655Zakj1NbWwtZllFaWhqzvbS0FIcOHUrqGA8++CBaW1vxwx/+sNN9li9fjqVLl8Ztf+ONN1BQUJB0eYmIKHe0rTrQ9hW5c/N7+PpjQ4uT09rXhW/3FshdZMk3d2WkSDkp2Xr44n//SD9qPhMA8PEugKcwfbSvi+3/2Yivcuhel8/nS2o/1WHTZrNh/vz5mD9/PpqbmwEAPXr0UHuYmOO1l+zg/ueeew5LlizBK6+80mX33UWLFqGysjL6s9frRVlZGSoqKlBcXJxyuYmIKDt5/WH866ta4MNtAIABY0/Bqcf1MbhU2ev9L2u77KrskORoXRQMndDpxDQAcNqwPvBwAfu07Kj24nBTIG57svUwuE8BjutXpGsZs1mjL4Stexu73EfNZwIACvOc+OaQznsBUupqmgPAh+8AAMad/G2MKM2d936kt2h3Uh61XVNTg88//xw2mw0jR45Ev379VD2+b9++cDgcca2YNTU1ca2dHT3//PO49tpr8eKLL2LGjBld7uvxeODxeOK2u1wuuFwuVWUmIqLsJwdk2OxHvx7DwsbvCz05nLCJzsOmTTl6A9pmd8Lm6PzC2ulywcWwmRa7wwmbI/7yMNl6cDh5fZUOnxRK+Pq3p+YzAQB+qa1eHXZ2pdWa03m0G63D4cyp936yf6vqCYK8Xi8uv/xyDBw4EFOnTsW3v/1tDBw4EJdddhmampqSPo7b7cbEiRPj1uZcv349Jk+e3OnjnnvuOVx11VV49tlncfbZXCSYiIi0FQzHBh9OEGQdnB8ofZzfx1hev/aT+QgBNAdyZyyhUfjZSUx12JwzZw7+/e9/49VXX0VjYyOamprw97//HR999BGuu+46VceqrKzE448/jieffBI7duzA/PnzsXfvXsydOxdAWxfYK664Irr/c889hyuuuAIPPvggTj31VBw6dAiHDh1SFXKJiIi6EpRiJ+aQZQGJSwdQjmBgN5ZXp1CoR4il9GdvzgWqu9G++uqrWLduHb71rW9Ft82cORO/+93v8N3vflfVsWbPno26ujosW7YM1dXVGDduHNauXYvy8nIAQHV1dcyam48++igkScKPf/xj/PjHP45uv/LKK/HUU0+p/VOIiIjiBMLxwTIgKShyqL4/S5SDmFZTFZRk+EOdz0KbDr1CbK5j1Oye6rDZp08flJSUxG0vKSlBr169VBdg3rx5mDdvXsLfdQyQb7/9turjExERqREIx1/sBcIyijxcnF4PWl6ssZHBeGwZTV2TX79A2Ohj2NQbzz+Jqb5Ne9ddd6GyshLV1dXRbYcOHcJPf/pT/PznP9e0cERERJmWsGUzQQAlbfACzVxkJb20mObDc5pXx7AZCMs8j+mA56/uqb5Nu3r1anz55ZcoLy/H4MGDAQB79+6Fx+PBkSNH8Oijj0b33bx5s3YlJSIi0pmiiLgxmwDDpp5sGrZt8sIvfXKaTZPphtVcpmfLJtAWZvM4W7NuOH4zMdVh8/zzz9ehGERERMYLSHLCboD+ECcI0ouWqzHYebGXNiXtlk2GzVQoitA9bDb6w+hfnKfrcxB1pDpsLl68WI9yEBERGa6zyTl8Ic7kqBuGTVOR0gyb6T4+VzUHJCg639PiuE0yQsqzHWzatAk7duyAzWbDmDFjMGHCBC3LRURElHG+zsImu9HqRsuAyDXr05duN1guE5SaBl9I9+doDoQhyQqcnFmbMkh12KypqcHFF1+Mt99+Gz179oQQAk1NTZg2bRr++Mc/ol+/fnqUk4iISHedhU1ZFgiEZY530oFDo4Ros3HMlBbCaYbFsMyWzVQ06tyFFmibKbjJH0afIo/uz0UUofrWxs033wyv14tPP/0U9fX1aGhowH//+194vV7ccsstepSRiIgoI1qCnXeX7SyIUnq0atlkF9r0KYqAlGZYTDes5iIhBBoz0LIJAA3sSksZprpl8/XXX8ebb76J0aNHR7eNGTMGK1euREVFhaaFIyIiyqSuxma2BiX0LnRnsDS5QauWTa2Ok8tCGgTFsKxACMFWZhWag1LaIT9ZmQq1RBGqWzYVRYHL5Yrb7nK5oOg9spmIiEgnIUlBMMEamxHNAU4SpAcHWzZNQ4tWSSHYlVatxtbMtTZ6A2EuT6MTwZmYE1IdNs844wzceuutOHjwYHTbgQMHMH/+fEyfPl3TwhEREWVKV11oAaCVM9Lqwq7RXCVs2UxfoIubLWokWquWOpeJyYEiFEX/9TxzCfNl91Sf4n/zm9+gubkZQ4YMwbBhwzB8+HAMHToUzc3N+PWvf61HGYmIiHTX0k3LZUtA4p1rHbAbrXloFRK1Cq25QAiR0bAJAPWt7EqrlfbfCPx6SEz1mM2ysjJs3rwZ69evx2effQYhBMaMGYMZM2boUT4iIqKMaA52fbdfVgT8YRkF7pRXDaMEnJqFTU0Ok9O0CokBLhWUtEyO14zguE3t8AZk91R9Y0qShLy8PGzduhVnnnkmzjzzTL3KRURElFHdtWwCbeM2GTa1pdVYS4dW/XFzmFYhkWEzeZkcrxkRGbfJ3gDpY8tm91SdmZ1OJ8rLyyHLPIkQEVH2UBSR1JhMThKkPadGIVGrFtJcptXyPn6GzaTVG9DKqChs3dRK+4CpMG0mpPoMf9ddd2HRokWor6/XozxEREQZ1xqSkMyE6t1NIkTqORycjdYsulr6R91xGDaTYcR4zQijnjfriIT/pXZU9wV65JFH8OWXX2LgwIEoLy9HYWFhzO83b96sWeGIiIgyoTWY3MVxMl1tSR2tlj5xahRac1VIUjQbO+gLSVxrMwlevwTZoGVi6g3ovpuN2q8iozBuJqQ6bJ533nk8eRARUVZJtsUyEJYhyQqcnI1GM5yN1hxaNWy1V5S2yYby3Q7NjpmNjGxdbA6EEZYVuHguS0v7rrOcLCgx1WFzyZIlOhSDiIjIOGq6D7aGZJTk8wJNK5rNRssb4WnRuot4S1Bi2OyGEeM1I4QAGn1h9OvhMawM2aB92FSYNRNK+tvS5/Phxz/+MQYNGoT+/fvjkksuQW1trZ5lIyIiyohku9ECgJ/j0TRlZ8umKegRNqlziiIMn6SH4zbT136sv8K0mVDSYXPx4sV46qmncPbZZ+Piiy/G+vXrceONN+pZNiIiooxQs1SDVpOoUButWjY5ZjM9WofD5gDHBHalyR9OalIyPdW3Mmymq33LpsywmVDS3WhffvllPPHEE7j44osBAJdddhmmTJkCWZbhcLCbBBERWVNQklVdJGi18D210WzMJrvRpkwIofnkV1wmqGtGdqGNaAlICEkK3E4OC0hV++8OLn2SWNLvrn379uH000+P/nzyySfD6XTi4MGDuhSMiIgoE4KSuvAYkNiNVktahU2tuuPmopagpHmrjD8kI6Tys5VLGkzSqmh0V16rY8tm95IOm7Isw+12x2xzOp2QJN65IiIi61J7QcwLaG1ptT4mWzZT59WpFdLLrrQJyYowzWtjhhZWK5MEWza7k3Q3WiEErrrqKng8R2etCgQCmDt3bsxamy+//LK2JSQiItIRw6ax2LJpvCafPsGnyR9G3yLOdtpRoy9k+HjNCI7bTE/7SYG0Wqc22yQdNq+88sq4bZdddpmmhSEiIso0tRcIklmuErOI3Y60L745G23qGv36BI4mvzla78ymQadwnwpfUEZQkuFxcv6VVLTvOiuzZTOhpMPm73//ez3LQUREZIiwypSjKG13s9mSph27zQYF6V2osTpSE5IU+FQs/aNGkz8MIQRs7OIcw2zjJBt9YZQWM2ymIiZscsxmQpx+ioiIcloqFwgSLyo0pUUYsYGBJhV6tWoCgCwL3caDWpWsCNO1+LIrbepiZqPl90JCDJtERJTTUgmbnAhCW1q0SrLxLDUNrfoGH7O14hmt0ReC2U4fDayjlEls2ewWwyYREeW0VC4QeFFhPgybqdG7VYutZrHMNF4zIjJuk9Rrf+NRCLZuJsKwSUREOS2VVga2bGqLXWCNEZRktAb17eba6AvzArwds7b0NpowBFtBxwnmOMQiHsMmERHltFSCI68nKBtkotXRjGMUjaKYaH3Njhg2U9Oxlwt7vcRj2CQiopyWStgUbNmkLFDXkplWtrrWYEaex+y8gbBp1tfsyKwtrmbX8fuDy5/ES3rpEyIiSo0vpG83tQI3T+XpSOVGNO9ea0ukuewJkFp36FwmhEBdhsZT1raEMLx/Rp7K1MzcetgSlCDJCpwOtkOp0bGLOL8b4vEKhYhIZ2PuXqfr8fesOFvX42e7VFopeT2Rno43YPwhGWEpcZNP+4lLuprEpDUoxbQy8CZM17wBqdPXXGstAQmBsIw8V26v5dho4u7EQrSti9qnyGN0USxDCMFutEngmZiIiHJaKtcG7EabnlRvwFS++EnS+/ImTNdqWzLbtbWuNYRBPfMz+pxmY/auqgyb6iT67uDkcfEYNomIdLZ92cyk9vOFJEy65y0AwEd3TWfLTIakts6mDgUhyqDa5syGzSPNwZwOm61BKW7mUrMxc8urGSX67uDMy/F4JUNEpLNUQmOB28mwmSGp3InmJBDpaX8DRpIVbNxZ2+m+QUmOtmg+dNF4eJyJu2KeNLgXehW6tC1olgqEZTQH9B1L3lFDawiyIuCw5+YyN1aYkbfJH4YQAjYuWpuURN8dzJrxeCVDREQ5LaWlT3hFkZb2N1ICYRmeJMfyeZyOTvd1OWy8QZOkIxlu1QTaWoHqWoPo3yMv489tBlYIm7Is0BqSUeTh5ygZib46eCMyHqecIiKinJbKItycBEI7IVmbSWq0Ok4uqDEgbALGhFyz8FogbALWKacZJGzZ5HdDHIZNIiLKaalcHKQSUCmxkEYzomp1nGwXlhXDJqqpbQnl5ORasiLQEsxst+VUeQMMm8lKFDZz8O3dLYZNIiKTyMWLMKMpikjp4oAzDmonqFFIDIQZNpNxpDlo2AVxWFLQYOK1JvXSEpAsE0K8fmuEYjNIdM9RizWDsw3DJhGRSYRNPlNhNkq1hdLss0paiT/U+dqZagS6WIOTjjK6K2tNc8DQ5zeClVoLW4JhdgVNVsKlTzJfDLMzPGyuWrUKQ4cORV5eHiZOnIiNGzd2um91dTUuueQSjBw5Ena7HbfddlvmCkpEpLNwuzFnbOXMjFRbKNmyqZ1AWKOwqVFozWaSrKCu1diw2daymlufn0zP/JsORQFaQ9Ypr5ESd6PNrfd2MgwNm88//zxuu+023HnnndiyZQtOP/10zJo1C3v37k24fzAYRL9+/XDnnXfixBNPzHBpiYj01b61jJOdZEaqLZucIEg7rRqNZfOHZbbIdKOuNQTF4FNLMKxYYmZWLVmpZROwVjg2UqKzDU9B8QwNmw899BCuvfZazJkzB6NHj0ZVVRXKysqwevXqhPsPGTIEv/rVr3DFFVegpKQkw6UlItJX+4DJbpqZkWpo5PT22hBCwKdRi6QQgE+jVtJsddhrji6sRs2GawRFEfBZrKXQKpMZGY2tmMkxbCGdUCiETZs2YeHChTHbKyoq8P7772v2PMFgEMHg0ZOa1+sFAITDYYTD1rrTRETZLRgMx/w/nJfc2oOUulAoBCHHX1gJRW73fwmiQ/iXwjZ+h2jAF5IgdfM6dlcX7TW2+OGx5+Y6jt2RFYHaJh9EijdY1NRDdw41tGBILw9sNlvKx7CK1pAEOaxteNOyLhJpag0gHObnqDuhsAShHK1bobSdz3LluyHZv9OwsFlbWwtZllFaWhqzvbS0FIcOHdLseZYvX46lS5fGbX/jjTdQUFCg2fMQEaUrKAOR0/L77/wDHmZNw7SvC9/uLZA71IUPQPV/M12q3NRdXbS3aVdGipST1NRDd3wAXvtci1LlJi3rIhEfgIPbtD1mtupYF5/vBXLlre3z+ZLaz7CwGdHxrpYQQtM7XYsWLUJlZWX0Z6/Xi7KyMlRUVKC4uFiz5yEiStf2g17gww8AABNOm4ohfQsNLlH2O9ISxKf7m+K2OyQZ+LDtaqtg6AR4nLFXcwUeJ04e2jsjZcxmXxxuwYGGri9YuquL9ooLXPjG4F6aljFbbD/oRU0a3WjV1EMyynoXYFj/orSOYQW7jrRib12rpsfUui4SmTy8D9w6HDeb1DQHseXr+pi6GNqvCMeX9jC4ZJkR6S3aHcPCZt++feFwOOJaMWtqauJaO9Ph8Xjg8XjitrtcLrhcLs2eh4goXcJ+dBi9AgfPURngcMiwOeK/Cm3K0ZueNrsTNkfsRZfNzvrRQnNISfj6t9ddXbTXGhawO5xw2LO/e6YasiJQ70/8Xk+WmnpIRq1Pxqgc+AwFFaT1uieidV0kElLsKMyB+kmHwyHDZj9atza7Eza7M2e+G5L9Ow2bIMjtdmPixIlYv359zPb169dj8uTJBpWKiMg4YandbLRGTxmZI1Kd34EzDqYvJCmaz3qpKECjL6TpMbNBXWvQdDMoB8JyTsxK2xq05qRVXP6ke4mWPuGyWPEM7UZbWVmJyy+/HJMmTcJpp52Gxx57DHv37sXcuXMBtHWBPXDgANasWRN9zNatWwEALS0tOHLkCLZu3Qq3240xY8YY8ScQEWkmdjZahs1MEAknr9fvcXRUg06hsK41hD5F8T2aclmN15yzvx5pDqAkP3tbgYQQ8Gs8OVCmaDVLdDZLdAOHWTOeoWFz9uzZqKurw7Jly1BdXY1x48Zh7dq1KC8vBwBUV1fHrbk5YcKE6P83bdqEZ599FuXl5dizZ08mi05EpLmQdDRghhk2MyLVCwNeUKTviE7LX9Q2B3NmzFQyFEWgtsWcYbPGG8Tw/tlbV4GwYvi6pqli2OxeolZMLosVz/AJgubNm4d58+Yl/N1TTz0Vt41r2hBRtmofMNsHT9IPv1GMoWcA8oVktAQlFHkMv8QxhQZfyLTr9mZ7XfktvO6rn2GzW4laNmWr3l3QkWFjNomI6ChFEQi3C5hBhk3KYnWt+gagw2nMupptanRqQdaKXi3cZmDpsGnR7r+ZJCUIm2a9sWMkhk0iIhMIdeg2G5Kse5GSC3g5kR69w+DhJoZNoK03mNnDXDrLsZidlVsHFaVtEifqXKJgmSiA5jqGTSIiE+j4pa4oQJCBk7JQWFZQ06xvwPCFZM5KC8AbkEzfJb85IGVtqLH638UeNl1LNLcC51uIx7BJRGQCibpbBUL80qLsc6gpkJFJU/Y3+PV/EpMze6tmhFknMEqX1cMab3h2LVGwlGQBha2bMRg2iYhMINFabD6OmTEtTlaXukyFwJrmgOlb9fRmlRBX25KdrdBBq7dshnP789OdQCevj9VvMmiNYZOIyARag/HBMtE20hZDY2bVt4Yy9r5WFOBAY+62bgYlGS0Ba5xDGlpDWdkaFLR4l0qGps4JIRCSE99MYItwLIZNIiIT8AbCcdua/Na4ULSylNfZ1LYYOePrutaMPt/+Bl9Whphk1Ldap7VQVgQa/fHnQCtTFAHZ4jOTcvxh54JS52uoco3SWAybREQG84fkhN2VvP5wzl4oZ0qiRbmTehzrRbXmQBh1Ge4uGQwrOJTFs512JdOvdbrqW63R5TdZHWcYtyKGzc511UODYTMWwyYRkcHqOrnIkhWBpiy72282iRblToYQDJxqfV3nM+R599S25mR36QaLzcZb35pd57psCGrZ8DfoJdE8CxEtHAITg2GTiMhgXU2OYZUJPqwqnTXRuJ5a8nwhSfe1NTt/bhmHvbn1OWoNSpab3KU5EIaUReEm1RtZZpJoHUlqk2joS0RzF7/LRQybREQGkmSly+5jNRZZusCq0pmtlHf9k7frSGvK42M1ef7alpxq3bTi+EchrFnuzmTDzahsCMx68XbxXg2GFcuvsaolhk0iIgPVtYa6XHPQH5J5l1RH6Yyr4kyNyWkNGteqGeEL5lbrZpPPmueMbBo2kA1BLRsCsx4CYbnbcZlW68auJ4ZNIiIDJbPoerauQWcGgTQmcvDzznVSdtca26oZsetI7rRuNvqtec7IprCZ6uRjZpINf4Me6pKY6dlqE3TpiWGTiMhAjUm0QPAOqT4URaQVGH2cBKJbzYEwDjWZYzZYX0hGtUnKoidJVuDrYvISM+uqa6LVZEOjILNmYsndJA5yErn/cRpdACKiXBWSkhvXkU0XYEbyhWLDYZM/3GnLZvtFuTtboLumOYhBvfJjthW4+bXa3q4jmV1Xszu7jrTimOI82O02o4uim65myTQ7SRYIhGXkuRxGFyVt2RA02LIZLyQpqEti4j5JFqhtDaJ/j7wMlMrc+K1IRGQQf5JdOCVZICwrcDnYGSUdY+5el9LjKl/8JOl996w4O6XnyEZNvnBSLQCZFAjLONDoR1nvAqOLopvmoLVvTjUHpKwIm9mAWTPeoaZA0q/LwcYAwybYjZaIyDDhrmYG6rgvZz4li/mqtsXoIiS0u7Y1KyZv6YyVWzaB+B4IZBxb9nYASIkQAvsakl8vuLY5yPcz2LJJRGQYNV2UeIc5fduXzYz+X5IVvPdlbaczAQclOdqi+dBF4+FxJm5pObZPAUb0L9K8rFbX6Auh3qQTZIQkBQca/BjcJztbN61+cdvdLJ9WkQ1BLRv+Bi0d9gaT7pEUsafWhzEDi3UqkTUwbBJlKb0vODg2LX02JP9Nzi/99LV/zx5o9MPlcABJ9NbzOB3wdNKtr8kXRp7TkdVjAFPxlcnGana0p64Vg3rlw5GF9Wb1WZKzJ2xa/72l5jsq2ymKwK4j6ntrVDf5MaRvQU5fM+XuX06U5VIdn5Ysjk1Ln5oLXXsWXLiYyb765LtCdSUkKTjSEkRpMcflRDT5wmhIYmkAI4UkBQezdOxmMGztLvedTchlNY4sOGfzJtpR+xp8Kd0IEQLYebgFJ5X11L5QFsExm0REBlETNrOxBcYotS1BtAS0a/lvW0eS/ZwjdteZu1UzYk9da1bMGNqeJCuWH48akqwdliPsWXCFnQ2BWQuBsJzWzNq1zUHUeLN/2aXOsGWTKEu1H5/WFV9IwqR73gIAfHTX9Jzu6pFpavIjv/S1IYTAlzXaTlzTEpBw2BvEMSVs3WwNSqg12Qy0nQmGFdQ0Z1e9hbJgIjFJFlAUYflWNWcWpM1cvskZGYokhMDWfY2drquczDJZALB1XyO+ObRXdPx/Ll1r5c5fSpRjUjmRFbidOXUCNJqarrHMmtrY3+DXtFUz4ouaZvQtcsOZ48vTqJmp0Qz21vuyKmxavVUzQlIE3BYPOk6HtcsPZMffkKpUhiJxmazEcvtbkYjIQGouC9lLM32+kKR5q2ZEMKxg52FzLvWRKZKsoLrJWl3FvP4wvAFrr0vZXraETTUzdZuVOwtuPGXD30DGYxMGEZFBZDn5CypZCNg5M2DKFEXgvwe8ul6MH2z0o0+RO2cnC6ppDqp6T5vFwUY/io9xGV0MTWRJ1syKsOnKgqCWDX9Dqj66azo27WmA1M05LdllstobO6hEkzJaBcMmEUVxkpPMUjO+KiwrOf3Fn67PDzfD69e/BWt7tReFHieKPLn39XrYohNgHPYGMbJUZMVSFWQeDrsNDrvN0q3NbmdufucEJRmfH2qBw26Hmq/drpbJam9XbQt6FrjQs8CdRimtIzffRUSUUDBLZgG0CjWLQ2fL2nNG2Ffvw4EGf0aeS5YFPt7XmDXLNyQrLCuoN/lyJ50JSwoafdnRldbiwxyjsmV9R4/Fw5rVy5+KsKxg695GVd/PailK24RBzVnUhb8rufcuIqIYre1mWGv2az9xCnWuOZj8F02zDpPa5ILD3gA+P9Sc0ef0h2Rs3dsIKQtmBk1Wgy9k6XHFdRYNyh1lS+tslvwZyHN338plZnlJtNJlE0lW8PG+xox830qywJa9jTHXYNmKYZMoxx1pPnqRdaTFGksWZAMhhKqWIKu2GhmptiWITw82GfLczQEJH+9vtHQXOjWs3jLY6MuOz5crS2YPzZaJafKSGL9nZnmu7KiHZMiKwMf7mzJ6LgtJCjbvbYgus5KtcuddRERxhBA42Hi0e+GR5kDWLKhtdg2+MILh5F/rRl8IgXBudc1MR31rCJ/sb4Ri4Nu5oTWMj/c3QsmBwGn17mDZ0nMgG8Z1O+w2y6+xGZFv8ZbN/Bxp2ZSVtrU0Gwy4qRsMK9j0dXYHTuuflYgoZW2B52iAURTrTvJhNXvqWlXtL4T6x+SqhtYQPt5nbNCMqG8J5UTgbA1a+0aIrIisuJnjtNtgt/iVXTZNSlNo4bDpcdlzYt1gI4NmRLYHzux/FxFRp5oSzM6ZaBtp61BTAPUt6r/YDjT40WTx7op6a2gNYes+c3VfrWsJ4ZMDTVkbOBVFZEWPCDU9DczKZrNlQddNa5e/PSu3bBa4s39GbTMEzYhsDpwMm0Q5LNGd/FybRTMTfCEp+u+Q14+texsQDMvx/9q99kEp/veBkIwP99ShvjUYc0xq0+gzX9CMqG0OYluWBk4pS/6msBmawjVg9UlpsqnrZqHbadnJjgos/j7qjqIIfLzfHEEzIhI49ZwJ1wjZf9uCiDqVKGz6Q9lxwWUmY+5ep/oxkUWik7Fnxdmqj59tmgNh0wbNiCPNQWyv9mLswOKsmTUUAATM+5qrYeXZdNsrdDtRD/NcQKuVTSHHbrch3+2Az4LdzLN5rWBFEdh2oCmlHkZ6C4bbJg2aWN4ra1r52bJJlKNkRaAxQZfZQFhmaxlZSiAsY+u+Rkiy+dPCoaYAvjrSYnQxNOXIkuDsyJJJaXrkWTskWL38HRXnuYwuQkqsWu5k7DjkxZFm886+7w/J2JJFy2dl1yeaDJeJkJIL4wgyobrJD7mTi/N99X6MPKZHhkuUnQJhGS/ccCqOeLv/YgtKcrRF86GLxsOTxNirY3sXQJKVnJjIIRFFEfhkf5OlxtvtqfWhR54LpcV5RhdFE06HHQ67zdStysnIlolprB7WemRZyLFqC2GRxd9Hnfm6rhXVjeafCLE1KOG/B7048dgSy/eEyc53Ehkmle6CarHLYPqEEPi6ztfp7w80+jC0b2HWXHwZQVEE9jX4sKu2FbIs4FHZHcbjdCT1mCPNQfxrVx2G9SvCgJI8y38pqbW7rhVeC05qtaPai5J8V9Z0k8p3O9Bi8eVDsmWsYKHbadnwn+92ZN33TnG+9cJzgceRNS397TW0hvDFYev0LKltDmJ3bSuO61dkdFHSkl2faCJKSqMv3OUAdC6BkjpFEdjf4MP7X9Xhi8MtnbYeaykYVrD9oBcf7KpHjTcAkS2Dz7oRCMv42qLLwUiywK4j1ix7IlZtvYnIpotru92GkgLrBRwA6GnRcnel2IIthD3z3UYXQXOSrGB7tdfoYqi2u7bV8usYW+8TQKa2fdnMpPbzhSRMuuctAMBHd01n19gMSzRWs6MmfxhlGShLtlAUgQONfnxd5zNsvb7WoIRP9jehKM+JoX0L0b+HJ6tbOvc3+Eyxlmaqqpv8GN6/KCtacnoVunGoybo3qHoVZNfFda8CtyknP+lOttUD0NbNvCjPaamWf6verOjK3nqfJWd5FQLYebgZE8t7G12UlPEKnzSVSmi0wcawmUGKIlDd5O92vyMtQQQlOalxg7lM/l9L5td1PtOsNdgSkLBtfxMK3A4M6VuIY4rzYM+SVpv2ai14Md2eEECDL5QVYzf7FFo7JPQpsnb5O+pd6MZXRhciBb0t/j7qTEm+y1ph04Jdf7siKwL7Grq/7jGrhtYwmvxhy9YLr/DJEO0Xpv+ipgV9ijwGliY7JZqsyR+S8fnhZjS0u0jvuLZjVBh4/8s6jB7YI+GsdLl+gyDSkrm7ttU0IbMjX0jG9oNe7PnfmI9jSqwfatoLmvR1V8OoVnCt5bkc6FngQqPPet29nA4b+hZm13dQcZ4TbqfdtOemRHrkObNmDHNHvQrcOGCRsONy2lGYRcvPAG039cIW+iwkUuMNMGwSqfF1/dHJaeqag2gOhLNuBjqjcW1H/TT5w/j0YJNl1k7zhWT890AT9jf4MHZgCfKz5ELC5bAhbJ3GgoSyoQttxMCe+ZYMm8eUZF/Lv81mQ98iDw42WiPgAEDfHtkV+NtLdixqMMmbT53eJE5CdxPP9SpwZd3wCytOIteR18LjNg0Pm6tWrcL999+P6upqjB07FlVVVTj99NM73X/Dhg2orKzEp59+ioEDB2LBggWYO3duBktM6QqEZdS3xC4DcbAxgJHHMGymKyQpaA1KaM5Ad509ta0oynOiyJO9d6MTqW8NYeu+BkuOFWz0hfHhnnpMKu+FQotP6AIAfYs82BvsfFZls7PZsmuM2jHFefiypsVSrWkAMLh3gdFF0EX/YmuFzWzoTt6ZPJcDBR5Htzcof/zcFtXHVnOTGAAev2JSl7/PpnNShBVnZu7ICutId8bQq43nn38et912G1atWoUpU6bg0UcfxaxZs7B9+3YMHjw4bv/du3fjrLPOwnXXXYdnnnkG7733HubNm4d+/frhwgsvNOAvoI66WmdTUQTqWkPYdaQ1putYUJLxVU0zQrKMgSX53ba65Hr3TaAtsLcGJbQGZbSGJPhCElqCckw3kZU/mpDUsVJZ2xEAvqw5On24w2FDkceJArcDhW4nCj2REGrPujuke+paLRk0I8KSgv0N2bGO6rG98i09SVBpcV5W3aix220Y3Lsg5tzQmWRbcIDUW3GSWTqof7Ena79Tehe44XLau+0+aIbWtMh3RjYb0b+HKSaoOb6063N/vyxsYc6Gdait/DcY+sl+6KGHcO2112LOnDkAgKqqKqxbtw6rV6/G8uXL4/b/7W9/i8GDB6OqqgoAMHr0aHz00Ud44IEHGDYNIoSArAhI//s3brG+XTeBthlvnXY7nHZb1nV96kwgLONQUwANvhC8Acl0Yw9kWaDJF44Ziwu0hdDiPCdK8t0YUJKXFa1p/Yo8qmd51PtiTs0anjYb0DdLJkMpcDtxXN+ipMINYJ6AA7R1nx1Rau210xIp612AfQ0+BMNdn6NSacEB1H1fdNeCY7MBwyy+fl1XIuG/rkNPoo5+8NuPVB9b7ff2S3NP6/L32TaePJFkQlyyM/qnI1tvrnSlVwqz65rpexuw9uRZhr3jQqEQNm3ahIULF8Zsr6iowPvvv5/wMf/6179QUVERs23mzJl44oknEA6H4XLFv5mCwSCCwaMnWq+3bY2dcDiMcNi6/Z8zISwraPSHIckCkqxAUo4GS0URkBQFigHr+X26vz76f5vNBofNBoe97Z/TboPjf0HU6bAj321POLmN1Xh9IdS3+CErCgqcAJzdh+yL1ujbHefF67+Z1H6KLKGxVUaeQ8Btt/4FxTE9XLCJQlQ3Jd990yx1YbPZUN67AMUee9ac/waVuOELuNEa6v7vSaUeAH3q4ri+hbALBeFuQpkVjTmm0BQtOKNKu+4e63TY4baLrPksJHJsiRvHlhh/kXrioO57UmRzPSTLlYH757n4Ohc4gd4FDsgqusGY5Xs7ok+++b63ky2PYWGztrYWsiyjtLQ0ZntpaSkOHTqU8DGHDh1KuL8kSaitrcWAAQPiHrN8+XIsXbo0bvsbb7yBgoLsHKdhLP3fUge3/Uv358gO+taF2no4oFM5rME8dcF60FeydXFQ53KY3X0n6/8cm979h/5PkgUyURdr167V/0mINGWe7+22/XUqSBp8vuRuuhvelt5xPJcQossxXon2T7Q9YtGiRaisrIz+7PV6UVZWhoqKChQXF6dabOrEd2boPzFNLnYBSYXedcF6SB7rwhx4fiIiomTwe7t7kd6i3THsL+3bty8cDkdcK2ZNTU1c62XEMccck3B/p9OJPn36JHyMx+OBxxPfT97lciXsdkvpKeFrahqsC/NgXZgD64GIiJLB74vuJZujDJvayO12Y+LEiVi/fn3M9vXr12Py5MkJH3PaaafF7f/GG29g0qRJDI5EREREREQmYug8upWVlXj88cfx5JNPYseOHZg/fz727t0bXTdz0aJFuOKKK6L7z507F19//TUqKyuxY8cOPPnkk3jiiSdw++23G/UnEBERERERUQKGdhiePXs26urqsGzZMlRXV2PcuHFYu3YtysvLAQDV1dXYu3dvdP+hQ4di7dq1mD9/PlauXImBAwfikUce4bInREREREREJmMTwoC1Kwzk9XpRUlKCpqYmThBERERERESkUrKZytButERERERERJSdGDaJiIiIiIhIcwybREREREREpDmGTSIiIiIiItIcwyYRERERERFpjmGTiIiIiIiINMewSURERERERJpj2CQiIiIiIiLNMWwSERERERGR5pxGFyDThBAAAK/Xa3BJiIiIiIiIrCeSpSLZqjM5Fzbr6uoAAGVlZQaXhIiIiIiIyLqam5tRUlLS6e9zLmz27t0bALB3794uXxjSn9frRVlZGfbt24fi4mKji5PTWBfmwbowD9aFebAuzIH1YB6sC/PI1boQQqC5uRkDBw7scr+cC5t2e9sw1ZKSkpx6Q5hZcXEx68IkWBfmwbowD9aFebAuzIH1YB6sC/PIxbpIpuGOEwQRERERERGR5hg2iYiIiIiISHM5FzY9Hg8WL14Mj8djdFFyHuvCPFgX5sG6MA/WhXmwLsyB9WAerAvzYF10zSa6m6+WiIiIiIiISKWca9kkIiIiIiIi/TFsEhERERERkeYYNomIiIiIiEhzDJuUszhc2TxYF+bBujAP1oV5sC7MgfVgHqwL8zB7XTBsUk7x+Xx47rnn8Prrr8Nms0GWZaOLlLWEEFAUpdOTIOvCPFgXmcPPhfmwLownhGA9mAjrwjyyoS6cRheAqCuKogAAbDYbbDab6sc3NjYiHA6jX79+AICCggJ89tln2LhxIyZPnozi4mJNy5vN1NSFECJuv7q6OjQ1NaFfv37o0aMH6yIN6X4uGhoa0NTUhKKiIvTt2xf5+fmsixSprYuO+zU0NODIkSNQFAWjRo3i5yINqX4uIvs2NTXBbrejR48eAPh9kapU6qH9fi0tLcjLy4PT2XaJynpITccAb7cn374UqY+WlhZ4PB64XC4ArIt05PL5iS2blDGKokCW5S7v6ndkt9tht9tVfTAjx37ttdfQu3dvnH322QCAcDgMALjmmmvQo0cPVFVVRcuVqyKtLMlIti4iQfODDz5AcXExSkpK4HK50K9fP5x//vn47LPPovvmel1EXv/I50LrumgvHA5jxYoVGDRoEAYNGoSzzz4bL7/8MoC2L7Ncr4uISD3oURctLS1YvHgx8vLyUFRUBIfDgdLSUowaNQpXXHFFdL9cr4v2nws13xmpfC7eeustTJo0CcXFxRg5ciTmzJmD//znP9Hfsy5ERs5Pf/nLXzBu3DgUFRXhm9/8JqqqqqLf2QDrAYiti2Q+DzabLVoXaoLmJ598gsmTJ8Pj8WD8+PFYsWIFgsFg9Pesi8xdz2bN+UkQpUBRFCHLslAURbfn2LVrl1i4cKH41re+JcaOHSvWrFkTfe7uHDx4UFRUVIjvfOc74vjjjxdCCCHLcvTxTzzxhCgrK9Ot7JlktrqIbHvvvfeEzWYTL730kjh8+LDw+/0J92VdqJPK5yIUColly5aJESNGiLfeeks0NTWJAwcOiE8++SSm7KwLddTWRTgcFps2bRKvvfaa+Pjjj0VdXZ347LPPxMSJE8XixYtjys66UCeVz8X27dvFscceK+bPny8OHDggNm3aJGbMmCGGDRsWU/ZsqQuz1sNbb70l+vfvL6qqqsS+ffvEiy++KEaNGiVuv/326GOzqR6E0L8uJEkSGzduFHPmzBEnnHCCGDp0qLjqqqvExx9/3OXjDh48KE466SRx4YUXil27dolnnnlGDBgwQNx2223RcrMu1Mv18xNbNimG6KSlq+O2yB2zZO7QhMNhbNiwAQsWLMA555yDb33rW5g/fz527drV5eP27duHbdu24cQTT0RraytqamqS+hsURcELL7yAvLw8VFZW4sCBAwCOdiGx2Ww488wzUV9fjw0bNiR1TCPoURftSZKEqqoqjBkzBk8//XT0ORNRUxeRcgwYMAButxtjxoxB//79IcsyQqFQ3L65WhehUAivvvoqrr/+ekyfPh3jxo1Dr169cN555wFAp+MvUvlc7N+/HytXrsSaNWtwxhlnAACKiopwwgknxJQ9V+siwu/3Y+nSpTj55JNx3HHH4ZJLLsHBgwc73V9tXTidTnzjG9/Ad7/7XYwfPx69e/dGU1MTtmzZggsvvDCm7LlaF/X19bjjjjtw+eWXY/r06Rg7diwGDRqEYcOG4de//nX0eTtK5XOxc+dO1NXV4Re/+AUGDhyIb3zjG5g/fz4OHjwIv98f7aVh9rrQ8zPx5z//GRdddBEmTpyI8847D3//+9+73F9tPYRCIbz22msYOnQobr31Vhx77LH4wQ9+gLvuugurVq1CIBCIdjs0ez0A+tWFEALPPvssLrzwQpxyyin42c9+hoaGhk73b2xsxBNPPAEhBCorK/GrX/0Kzc3NmDlzJr7++utOH/evf/0Ln3/+OX73u99h6NChuPTSS7F8+XI89thj8Pv9OV8XmbyezZbzE8ButNRB5EMXudDdvXs3pk2bhn//+98A2j6ku3fvxosvvohFixZh6dKlALqeCSsUCmHDhg2orq7GpEmTcOGFF2Lnzp2oqKhAbW1tp4874YQT8Mgjj+DnP/85BgwYgJaWlmgZu7J//3488MADeOyxx+BwOBAMBuHz+WLKeOyxx6K8vDz6d5mRHnXR3u7du/HLX/4SoVAIe/bsiT5nIqnURf/+/REKhXDOOeegf//+mDp1KlasWBH3BZmrdREKhfD000/j//2//4cZM2bgxz/+MZ555hncf//9AACHw5HwcanUxbZt29DU1ITa2lqMHDkSY8eOxQ9/+EO8/fbbMfvlal0AbUHz5ptvxsaNG/HDH/4Q9957L77//e932S0p1XNUhBACf//731FWVhYT/IHcrQtZlrFz504UFhZi6tSp+OlPf4rrr78ee/bsid4oSfT6plIXo0ePRiAQwLPPPgsAOHDgAP7whz/gsssuQ35+fvSxZq8LvT4Tr732Gu644w4MHjwY119/PYYPH46rr74aL730UqePUVsPNpsN9fX1KC0tjdkeDocRCoWwcePG6Daz1wOgT10oioKnnnoKK1aswOjRo/GjH/0IX3zxBb7zne/EdDVur0ePHrj66qvxy1/+EldddRW+973vYfXq1SguLsaf/vSnTp9n27ZtGD58OHr16hXdfs455yAYDOLdd9+NbsvVusjk9Wy2nJ8AsBttLgsEAnHb3nnnHXHjjTcKIdq6nT7zzDNi4sSJ0d/7/X7xs5/9TEyZMkUMGTJE2Gy2bp9HURSxc+dO0djYGN12+PBhUVxcLJ599tmkyjp16lRRWVmZ1L4zZ84U9913nxBCiI8++kjYbDbR0tISt9+MGTPETTfdJEKhUFLH1VOm6qK9Cy+8UNx3331i6tSpYtGiRUk/Tk1d3HDDDeKVV14RmzdvFk8++aTo06eP+NGPfhTt0hyRi3URDofFwoULxaxZs1Iua7J18cgjj4iCggIxYsQI8de//lXs2LFDXH311aKgoEDs3r07Zt9crAshhPjrX/8qBg4cKA4fPpxSWdV8LtobO3as+MlPfhL3mRAid+uio1/84hdi+PDhSe+vpi4++OADMW7cOOF0OoXD4RBDhgwR//rXv+L2M0tdZKoegsGg+P73vy/OOussIUmSEEKI5uZmcdZZZ4k5c+YkVdZk6+G5554TDodD/PWvfxVCCPHll1+KadOmCZvNJh5//PGYfc1SD0Jkri68Xq8oKysTd955Z3Tb/v37xYgRI8Ty5ctVlbmsrEw88cQTnf7+lltuEdOmTYvWeUSPHj3EY489FrMtF+si09ezVjs/dYYtmzlICIFTTz0VS5Ysifvd9u3b8dvf/hZAWzfLF154ARdffHH09x6PB6eddhruvfdeVFVVwe12o7W1tcvns9lsGDFiBEpKSqLbDh06hObmZowaNarLx0ZaFQoLC9Hc3BzXDbOjl19+GR6PB/Pnz48+3uFw4IUXXsDWrVshSVJ0X6fTCUVROr0zmAmZrouIP//5z6iursatt94Kt9sNr9fb7V1utXUBAPfffz/OPfdcTJgwAVdffTX+8Ic/4IUXXsCXX34Zc8xcrAun0wmPx4N3330XF198Mc455xwsWLAAR44c6basauvC7XYjGAzi5ptvxjnnnINRo0bhySefRElJCV566aWYLru5WBcA8MQTT+D888/H2rVrMWPGDJx99tldtuBEpPK5iNi9eze+/PJLnHXWWTETeEQ+i7laF+J/s2hGXoenn3462r28K6nUxR/+8AcMHDgQ69atw86dO3HllVfizDPPxHvvvRctC2B8XWS6HtxuNzweD5qamqK9LOx2O7Zs2YLJkyd3+Vi19XDRRRfhjjvuwDXXXAO3242RI0fiRz/6EQoLCxEIBGK+m4yuB8CY74r9+/fjhz/8YfS4gwYNQnl5OV599dVOh1xEXrdIfTz77LNoaWnBmWee2elzeTweSJKE5uZmAEeHc+Tn56OlpSXn6yKT17OAdc5P3WHYzEE2mw35+fkJ3+hlZWVwOp2QJAl2ux3r1q3DtGnTYh573nnnYcqUKSgvL0coFOpy3EB7dXV1uPTSS6Njlp544gmceOKJ3ZYVaOsS0tLSAr/fH7dPZFYwAFizZg3+9re/oU+fPsjLy4t+Kd544414+umn4fP5YspTUlICt9udVPn1YERd+Hw+3HHHHVi0aBHcbjcKCgrQ3Nwc89p0Vlag+7poLzJFd8SJJ56IwsJC7Ny5M2b/XK2L8vJynHPOORgyZAimTJmCjRs3YuLEifjiiy+6LSvQfV1EvogGDx4Mm82G4cOHw2azRW+6lJeX48CBAzFfULlWF5HXaMeOHfjXv/6Fl156CRUVFTj11FMxZ84cVFVVxdykSlRWQN3nIvKcL730EsrKynDccccl3D/X6qL94yL/vvjiC3z11Ve44IILknockHxdfPrpp3jppZdw991344wzzkBZWRmWLFmCUaNG4bnnnouOUQOMrwsj6uHOO+/EsGHDcPrpp2PSpEkoLy/HxRdfjEsuuaTbsgLJ14PD4cAdd9yBzz//HLt27UI4HMaVV14Jt9uNPn36xJyzjK4HIPN1kZ+fjwEDBmD16tUA2oLFf//7X2zatAk7duxAU1NTp+UUQsBut2Pnzp245ZZb8MADD6CsrCxmv/YzDZeXl6OhoSGuS6jdbkdhYWHMeo65WBcRel7PRljp/NQdrrOZo0pLS+H1ehEKhWLenD169IAkSWhoaEBdXR0A4Jhjjol5rPjfoOTIOlg1NTU49thju31Oj8eDk046CePGjcPXX3+NJUuWoG/fvvje977X6WMiLZM9e/ZEdXU1WltbY+4oAbFrR/3kJz/BpZdeipKSEpSWliIcDuPcc8/F0qVLcd1110XvlttsNjQ3N+PYY4+F0+mMbjNCputixYoV0ZY0oG0in6ampm6nzFZTFx1fz8jPH3zwAex2O/Lz8wEcPfnmal3MmDED3/3udzFgwAAIIXDjjTdiypQpePjhh3HfffehqKgo4ePUfi4mT56MY445Bps2bcKsWbOi69cdOXIE/fr1Q15eXs5+LiL7ejwebN26Fb/85S8xa9YsAG139Z988kmcc845GD58eMLHq62L9o957bXXcMopp0TXAe64f67VRaLHvfTSSxg+fHhSj1FbF/v374fNZot+HiKvbzgcjrbsRZihLjJdD2PHjoXdbkdeXh6+973vQQiB1atXo6SkBAsWLIiexztSWw9CCOTl5SEvLw+9e/cGADz++OPo3bs3Ro8eDSB3vysi+/7f//0f7rvvPkyYMCF6HTNjxgx89NFHOHLkSPR1S/TYhoYGnHfeebjmmmtilliKaF8XU6ZMwdKlS7F161YMHz4cDocD7777LhRFQXl5eczjcq0u2svE9azVzk9dYdjMUWVlZdi/fz98Ph/cbnf0zdmjRw/Y7XbU1dWhpqYGffr0gdfrxaBBg6KPjbyJi4qKUFBQgOrq6m6fTwiBoqIi/PSnP41uu/HGG/HrX/8ao0aNwogRI6J3+9t/SCL/79OnD3bt2hUdVB3h9/vx5ptv4vjjj8fIkSNx+umnx/ze6/WiqKgoOiOtLMtwOp3YvHkzBgwYEHfyNEIm68Lr9WLlypU4/vjj8cknn+DAgQPYsWMHJEnCH//4R6xbtw5nnnlmNHi2P/ElWxcjRozAqFGj8NVXX2H79u0YPHgwBgwYgE8++QSLFy/GGWecgXHjxgFou6Odq3UReb4IIQR69uyJc845B1u2bMHhw4dRVFSU1uciUhclJSW45pprsHr1ahQVFeH888/Hww8/jPz8fFRUVESPmYt1oSgK7HY7BgwYgHA4HHN3uqKiAk899RS+/vprDB8+HIqiRFvcOj5XsnURueDYt28f9uzZg/PPPx+FhYXRckT+zlysi/Yiz/H6668nDOTt9+n4XN3VxfDhwzF69GicfPLJGDRoEG666Sbcf//9KC8vx+OPP47a2lp85zvfiZ7/zFIXmaqHyHEfeeQRfP7553jkkUcwadIkAMCgQYOwdOlSfOc738HUqVM1+UzYbDa88847AIBRo0bhn//8J5YvX45rr70WJ554IoQQOftdEdn3yiuvxNChQ7F169boDKRr167Fl19+GW3V6/hdYbPZEAwGcdFFF2HkyJFYtGhRNLhEdKyLk046CbNmzcJPf/pThMNhTJgwAT//+c9x2mmn4aSTTgLA7229r2eten7qCrvR5qhBgwahtbU12j898sEoKipCfn4+Dh8+DJfLhYKCAtTX18fsE5GXl4fi4uLo0gBdjflr/4GLdIcZN24cGhsbo2MD2n9hRY4V2Tc/Px9erxebNm3Ce++9h6+++goAcPDgQVxxxRV4/PHHY/aPcLvdGD9+fMwdIAD429/+huLi4ujYBSPvAmWyLpqamnDjjTeivLwcAwYMwKWXXorp06djxIgReOqppzB9+nQARxcfbn+sZOvid7/7HYC2YP/KK69g+vTpGDp0KK644gpMmzYNv/3tb1FaWho9Xq7WRUeRgB8Oh+H3+2PuZqb6uXjiiSeix7/zzjuxePFiPProozjhhBPw0Ucf4Te/+Q0mTZoU7Uabi3UReZ9PmDABLpcregccaPvyLykpib7e7afHT7UuIheGf/vb32C326M3XjoeNxfroj273Y49e/Zg7969OOWUU1BQUJBwyYL2x0q2Lp588kkAQK9evfDoo49i/PjxuOSSSzBhwgS8/vrrePjhh3HuuedGn88sdZGpeohse/PNNzF06FB84xvfiP5u7NixcLlc0W6WWnwmgLZu7BdeeCGGDBmCn/zkJ6isrMRdd90FAKarB8CYz8S3v/1t3HLLLbj55psxatQobN++HaWlpRg4cCCA+O8KSZLws5/9DHa7HQ8++CBKSkpQXV2NL7/8Eo2NjQCA6urquLp4+OGHcc011+D222/HhAkTUFhYiHvvvTfaWwzI7brQ+3rWquenrjBs5qgBAwbA7/dHPxiRD4PD4UCvXr1w+PBhDBo0CDabLbomU6IPZ+/evROuQydJEurq6qIXVnv27MGOHTsAtI03+PTTT7Fy5UoMHDgw2k0mEAjgX//6Fz799NPohyWyfMnSpUvx4Ycf4vLLL8fpp5+OdevWAQB69uyJu+66KxqSOt61y8vLw0svvYRFixZBCBHtYlBSUoLvfe970btgRspkXZSVleEXv/gF/vjHP+I3v/kNbr75Zpx11lkoLi7GiBEjohfewWAw5bqInPCOO+443HPPPdi2bRvq6+tx8OBBVFVVoV+/fqyL/30udu/ejU8//RRA23v31VdfxRNPPIGTTz45epcynbqIfC4URYHb7cZ1112HTz75BM3NzXjvvfcwdepUCCHgcrlyvi5mz54NIQRWrFiB1tZWBINBPPzww+jbt2/0YluLc1Tkxtc111yDHTt2xC3nEWndzOW6iFyUbdiwAW63G2PHjgUQexGVzudixowZ0eNMmDABK1euxJ49e9DY2IhNmzbhoosuAmC+ushUPQQCAQDAqaeeinfffRdPPfUUgLbX/JFHHoHNZot2K/f7/Wl/JgDgkksuwbZt29DQ0ID9+/dj/vz50e9zh8NhqnoAMv+ZaGpqwp49e6I/r1y5Ei+//DJ+8IMfoE+fPhBCRD8Tn3zyCWw2G1577TU88sgjePPNN/HNb34T+fn5GDRoEGbNmoUPP/wQQOLvit69e2PRokXYvXs3/H4//v73v2PkyJFQFIXfFdD/etaq56cuCcpJ7733njj11FPFO++8I4QQ0emSP/vsMzF27Fjx6KOPCiGEmDJlirjtttuEEG1TPrcXCoVERUWFuOGGG4QQQjQ0NIhgMCiEEOKxxx4TEydOjE7R/PLLL4tp06aJM844Q5xwwgli1KhR4pprrhG7du2KHm/btm1i5MiR4tZbb41u+/zzz8Urr7widuzYIerr62Oev+NSAR3L19m+fr9f7N+/X/h8vm5epczIVF18+OGHMY+NvCZr1qwRI0eOFK+++mr0eHrVRcftuVoXkc/F888/L0455RQxdepUMX78eDFy5EixYMGCmGnV9ayL9r/L1br497//Hd3/xRdfFKeffroYNmyYKCsrEzNnzhT/+c9/or/X8xzVXq7WReQcFXndLrjgAnHiiSfGLMMQOW4ufi4yVQ8ffPCBEEKIPXv2iB//+Mdi7Nix4oQTThCDBw8W3/zmN8Xf/va36PH4XZGZuti5c6eorKwUp5xyihg7dqwYPXq0+M1vfiOEENHPR6Qubr75ZiFEWz38+c9/Fu+8847Ytm2bOHLkSMzzdywP68L817NmPj91hWM2c1R5eTnC4TBee+01nH766XC5XACA5557DpIkYfz48QDaxiy9+eabOHz4cHTR5bq6Ojz33HN4/fXXsWHDBqxfvx4vvPACxo0bh5UrV+KEE05Az549UVhYGJ0yfdKkSbj00ksRCARQWlqKYcOGYfjw4ejRo0e0f73X60W/fv2iU1MrioLjjz8exx9/fEzZxf/u3ERa4RKNL+yo/e/y8vJi+uwbLVN10fH1ifx8wQUXYObMmejbt6/uddGxe0eu1kXkc3HKKafgqquuQjgcxsCBAzFixAiMGDEC+fn5rIsM1wUA/OAHP8D48ePx+eefo1+/fhgxYkS01UDvc1R7uVoXkdfHbrdDkiTcdNNNKCoqiqmjiFz8XGSqHiItiuXl5bjvvvvw/vvvo6WlBccccwyOO+449O/fn+enDJ+fBg4ciIqKCgwdOhTHHHMMRo8eHX2NI69hpC4iswUnqgfg6JjcyGvMurDO9azZ66IzDJs5atCgQbj00ktx7733wu/3o7y8HF988QW2bNmC6667DqeeeioA4LzzzsPWrVvxhz/8AZWVlQDaxpQdPnwYPXv2xLJly1BWVoYePXpgxIgRGDZsGIC2dbMiTf1CCJSVleHaa6/tskx5eXkYO3YsTj31VAghYj58iSaqiUj2As6sMlkXQPzrV1RUFJ31NHLiY11k5nNRXl6OuXPndlkm1kVmPhcRnV2gAayLTNaF0+mMdjFOJBfrwoh6KCgoiOnW11Eu1gOQ+booLCzEzJkzMXPmzE7L1L4uIrqawCmCdcHrWb3ZhDBrB1/Sm6Io2LBhA1auXImmpiYMHDgQZ599Ni644ILonSGgbVHZ+++/Hx999FHcmMjujt/+g9N+coeOJ79cl+m6oM4Z8blof6eZn4ujjPpcJLpAy3VG1gXPXUcZUQ9CiLjWMMp8XQghYs5NrIujeD1rbgyb1K2WlhasX78exx13XHRZgMgiwO2n2OYHTn+sC/NgXZgH68I8WBfmwHowD9aFebAujMGwSdGWlUhTP+8iG4d1YR6sC/NgXZgH68IcWA/mwbowD9aFOTFsEhERERERkeYY+YmIiIiIiEhzDJtERERERESkOYZNIiIiIiIi0hzDJhEREREREWmOYZOIiIiIiIg0x7BJREREREREmmPYJCIiIiIiIs0xbBIREREREZHmGDaJiIiIiIhIc/8fJhMqPBwKaFkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 5))\n",
    "\n",
    "\n",
    "# plot violin plot\n",
    "ax.violinplot(res_prop_all,\n",
    "                  showmeans=True,\n",
    "                  showmedians=False)\n",
    "ax.set_title('')\n",
    "\n",
    "ax.yaxis.grid(True)\n",
    "ax.xaxis.set_ticks(np.arange(9))\n",
    "ax.set_xticklabels(['','U(1.3,1.4)','U(1.4,1.5)','U(1.5,1.6)','U(1.6,1.7)','U(1.7,1.8)','U(1.8,1.9)','U(1.9,2.0)','U(1.3,1.8)'],rotation = 5)\n",
    "# ax.set_xlabel('Agent')\n",
    "ax.set_ylabel('Proportion of resistant tumor')\n",
    "plt.savefig(\"/Users/yaoyao/Desktop/research/cancer/ICC_decision_new/prop_resistant.eps\", format='eps', dpi=1000)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f23509f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
